Towards Risk‐Free Trustworthy Artificial Intelligence: Significance and Requirements
Given the tremendous potential and influence of artificial intelligence (AI) and algorithmic
decision‐making (DM), these systems have found wide‐ranging applications across diverse …
decision‐making (DM), these systems have found wide‐ranging applications across diverse …
Negation of the quantum mass function for multisource quantum information fusion with its application to pattern classification
F Xiao, W Pedrycz - IEEE Transactions on Pattern Analysis and …, 2022 - ieeexplore.ieee.org
In artificial intelligence systems, a question on how to express the uncertainty in knowledge
remains an open issue. The negation scheme provides a new perspective to solve this …
remains an open issue. The negation scheme provides a new perspective to solve this …
Generalized divergence-based decision making method with an application to pattern classification
In decision-making systems, how to address uncertainty plays an important role for the
improvement of system performance in uncertainty reasoning. Dempster–Shafer evidence …
improvement of system performance in uncertainty reasoning. Dempster–Shafer evidence …
Emotion recognition by correlating facial expressions and EEG analysis
Emotion recognition is a fundamental task that any affective computing system must perform
to adapt to the user's current mood. The analysis of electroencephalography signals has …
to adapt to the user's current mood. The analysis of electroencephalography signals has …
Multiview Classification Through Learning From Interval-Valued Data
The classification problem concerning crisp-valued data has been well resolved. However,
interval-valued data, where all of the observations' features are described by intervals, are …
interval-valued data, where all of the observations' features are described by intervals, are …
Deep Multiview Module Adaption Transfer Network for Subject-Specific EEG Recognition
Transfer learning is one of the popular methods to solve the problem of insufficient data in
subject-specific electroencephalogram (EEG) recognition tasks. However, most existing …
subject-specific electroencephalogram (EEG) recognition tasks. However, most existing …
State-input affine approximate modeling based on a differential neural network identifier
A Guarneros-Sandoval, M Ballesteros… - Applied Mathematical …, 2024 - Elsevier
This work presents the development of a state-input affine differential neural network that
approximates a class of nonlinear systems with uncertain dynamics and perturbations. The …
approximates a class of nonlinear systems with uncertain dynamics and perturbations. The …
A Bilevel Optimization Approach for Tuning a Neuro-Fuzzy Controller
This work presents a methodology to solve optimization problems with dynamic-size solution
vectors containing continuous and integer variables. It is achieved by reformulating the …
vectors containing continuous and integer variables. It is achieved by reformulating the …
Forearm sEMG data from young healthy humans during the execution of hand movements
This work provides a complete dataset containing surface electromyography (sEMG) signals
acquired from the forearm with a sampling frequency of 1000 Hz. The dataset is named …
acquired from the forearm with a sampling frequency of 1000 Hz. The dataset is named …
[PDF][PDF] Free-Space Optical Communication with an Optimized Lipschitz Exponent for Biosignal Telemetry
M Chokkalingam, C Murugaiyan - Measurement Science Review, 2023 - sciendo.com
Healthcare monitoring is a rapidly developing network in the field of advanced medical
treatment. The network combines the ideology of wireless communication, signal …
treatment. The network combines the ideology of wireless communication, signal …